As computer technology improves, computer systems with more powerful processor(s) and larger storage unit(s) become more commonplace. With this growth of processing power and storage unit size, implementation of digital imaging technology also becomes more practical. For example, higher resolution images may be processed in a shorter time period.
An advantage of digital imaging technology is the ability to render the digital images. Rendering an image generally involves producing a synthetic or virtual image using a computer. For example, different light sources may be applied to a scene from different angles and with different intensities to generate a virtual view of the scene. One type of rendering is image-based rendering (IBR), where rendering techniques are applied to a set of sample input images (e.g., digital pictures taken by a digital camera or conventional pictures scanned into a computer).
Central to many IBR systems is the goal of interpolating accurately between the sample images in order to generate novel views. In IBR, rendering a desired pixel is often equivalent to interpolating intensity values of some input pixels. Such an interpolation, however, depends on the correspondence between the rendered pixel and those pixels from the input sample images. Often, accurate correspondence between these pixels can be obtained if a large number of input images or an accurate geometric model of the scene is available.
When such information is unavailable, one solution is to perform stereo reconstruction or to establish correspondence between pixels of the input images. However, state-of-the-art automatic stereo algorithms are inadequate for producing sufficiently accurate depth information for realistic rendering when using a relatively sparse set of images of a complex scene. Typically, the areas around occlusion boundaries in the scene have the least desirable results, because it is very hard for stereo algorithms to handle occlusions without prior knowledge of the scene.
Accordingly, current solutions fail to produce virtual views free of aliasing when using a relatively sparse set of images of a complex scene.